246 lines
6.9 KiB
Plaintext
246 lines
6.9 KiB
Plaintext
{
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"cells": [
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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],
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"id": "746c5b9e4c0226e7",
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"%reload_ext autoreload\n",
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"%autoreload 2\n",
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"import pickle\n",
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"import numpy as np\n",
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"import winkelumrechnungen as wu\n",
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"from itertools import product\n",
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"import pandas as pd\n",
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"from ellipsoide import EllipsoidTriaxial\n",
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"import plotly.graph_objects as go"
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# ellips = \"KarneyTest2024\"\n",
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"ellips = \"BursaSima1980\"\n",
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"# ellips = \"Fiction\"\n",
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"ell: EllipsoidTriaxial = EllipsoidTriaxial.init_name(ellips)"
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],
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"id": "7b05ca89fcd7b331",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"def deg_range(start, stop, step):\n",
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" return [float(x) for x in range(start, stop + step, step)]\n",
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"\n",
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"def asymptotic_range(start, direction=\"up\", max_decimals=4):\n",
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" values = []\n",
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" for d in range(0, max_decimals + 1):\n",
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" step = 10 ** -d\n",
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" if direction == \"up\":\n",
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" values.append(start + (1 - step))\n",
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" else:\n",
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" values.append(start - (1 - step))\n",
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" return values"
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],
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"id": "61a6b14fef0180ad",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"beta_5_85 = deg_range(5, 85, 5)\n",
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"lambda_5_85 = deg_range(5, 85, 5)\n",
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"beta_5_90 = deg_range(5, 90, 5)\n",
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"lambda_5_90 = deg_range(5, 90, 5)\n",
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"beta_0_90 = deg_range(0, 90, 5)\n",
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"lambda_0_90 = deg_range(0, 90, 5)\n",
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"beta_90 = [90.0]\n",
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"lambda_90 = [90.0]\n",
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"beta_0 = [0.0]\n",
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"lambda_0 = [0.0]\n",
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"beta_asym_89 = asymptotic_range(89.0, direction=\"up\")\n",
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"lambda_asym_0 = asymptotic_range(1.0, direction=\"down\")"
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],
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"id": "f7184980a4b930b7",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"groups = {\n",
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" 1: list(product(beta_5_85, lambda_5_85)),\n",
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" 2: list(product(beta_0, lambda_0_90)),\n",
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" 3: list(product(beta_5_85, lambda_0)),\n",
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" 4: list(product(beta_90, lambda_5_90)),\n",
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" 5: list(product(beta_asym_89, lambda_asym_0)),\n",
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" 6: list(product(beta_5_85, lambda_90)),\n",
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" 7: list(product(lambda_asym_0, lambda_0_90)),\n",
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" 8: list(product(beta_0_90, lambda_asym_0)),\n",
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" 9: list(product(beta_asym_89, lambda_0_90)),\n",
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" 10: list(product(beta_0_90, beta_asym_89)),\n",
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"}"
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],
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"id": "cea9fd9cce6a4fd1",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"for nr, points in groups.items():\n",
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" points_cart = []\n",
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" for point in points:\n",
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" beta, lamb = point\n",
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" cart = ell.ell2cart(wu.deg2rad(beta), wu.deg2rad(lamb))\n",
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" points_cart.append(cart)\n",
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" groups[nr] = points_cart"
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],
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"id": "17a6a130782a89ce",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"results = {}\n",
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"\n",
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"for nr, points in groups.items():\n",
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" group_results = {\"ell\": [],\n",
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" \"para\": [],\n",
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" \"geod\": []}\n",
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" for point in points:\n",
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" elli = ell.cart2ell(point)\n",
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" cart_elli = ell.ell2cart(elli[0], elli[1])\n",
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" group_results[\"ell\"].append(np.linalg.norm(point - cart_elli, axis=-1))\n",
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"\n",
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" para = ell.cart2para(point)\n",
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" cart_para = ell.para2cart(para[0], para[1])\n",
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" group_results[\"para\"].append(np.linalg.norm(point - cart_para, axis=-1))\n",
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"\n",
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" geod = ell.cart2geod(point, \"ligas3\")\n",
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" cart_geod = ell.geod2cart(geod[0], geod[1], geod[2])\n",
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" group_results[\"geod\"].append(np.linalg.norm(point - cart_geod, axis=-1))\n",
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"\n",
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" group_results[\"ell\"] = np.array(group_results[\"ell\"])\n",
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" group_results[\"para\"] = np.array(group_results[\"para\"])\n",
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" group_results[\"geod\"] = np.array(group_results[\"geod\"])\n",
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" results[nr] = group_results"
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],
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"id": "c3298ea233bca274",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# with open(f\"conversion_results_{ellips}.pkl\", \"wb\") as f:\n",
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"# pickle.dump(results, f)"
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],
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"id": "e1285860be416ad3",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"# with open(f\"conversion_results_{ellips}.pkl\", \"rb\") as f:\n",
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"# results = pickle.load(f)"
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],
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"id": "d26720e34595ccbc",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"df = pd.DataFrame({\n",
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" \"Gruppe\": [nr for nr in results.keys()],\n",
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" \"max_Δr_ell\": [f\"{max(result[\"ell\"]):.3g}\" for result in results.values()],\n",
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" \"max_Δr_para\": [f\"{max(result[\"para\"]):.3g}\" for result in results.values()],\n",
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" \"max_Δr_geod\": [f\"{max(result[\"geod\"]):.3g}\" for result in results.values()]\n",
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"})"
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],
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"id": "4e2e55e4699ec81e",
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"outputs": [],
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"execution_count": null
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": [
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"fig = go.Figure(data=[go.Table(\n",
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" header=dict(\n",
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" values=list(df.columns),\n",
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" fill_color=\"lightgrey\",\n",
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" align=\"left\"\n",
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" ),\n",
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" cells=dict(\n",
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" values=[df[col] for col in df.columns],\n",
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" align=\"left\"\n",
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" )\n",
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")])\n",
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"fig.update_layout(\n",
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" template=\"simple_white\",\n",
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" width=650,\n",
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" height=len(groups)*20+80,\n",
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" margin=dict(l=20, r=20, t=20, b=20))\n",
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"\n",
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"fig.show()\n",
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"# fig.write_image(f\"conversion_results_{ellips}.png\", width=650, height=len(groups)*20+80, scale=2)"
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],
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"id": "c2fa82afef2d6e0e",
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"outputs": [],
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"execution_count": null
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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